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Quantum computing is currently considered to be a new type of computing model that has a subversive impact on the future. Based on its leading information and communication technology advantages, IBM launched IBM Q Experience cloud service platform, and achieved phased research results in the quan…

Quantum computing is currently considered to be a new type of computing model that has a subversive impact on the future. Based on its leading information and communication technology advantages, IBM launched IBM Q Experience cloud service platform, and achieved phased research results in the quantum simulator and programming framework. In this paper, we propose a quantum solution for the 3-SAT problem, which includes three steps: constructing the initial state, computing the unitary implementing the black-box function and performing the inversion about the average. In addition, the corresponding experimental verification for an instance of the Exactly-1 3-SAT problem with QISKit, which can connect to IBM Q remotely, is depicted. The experimental result not only show the feasibility of the quantum solution, but also serve to evaluate the functionality of IBM Q devices.

Timely and precise yield estimation is of great significance to agricultural management and macro-policy formulation. In order to improve the accuracy and applicability of cotton yield estimation model, this paper proposes a new method called SENP (Seedling Emergence and Number of Peaches) based o…

Timely and precise yield estimation is of great significance to agricultural management and macro-policy formulation. In order to improve the accuracy and applicability of cotton yield estimation model, this paper proposes a new method called SENP (Seedling Emergence and Number of Peaches) based on Amazon Web Services (AWS). Firstly, using the high-resolution visible light data obtained by the Unmanned Aerial Vehicle (UAV), the spatial position of each cotton seedling in the region was extracted by U-Net model of deep learning. Secondly, Sentinel-2 data were used in analyzing the correlation between the multi-temporal Normalized Difference Vegetation Index (NDVI) and the actual yield, so as to determine the weighting factor of NDVI in each period in the model. Subsequently, to determine the number of bolls, the growth state of cotton was graded. Finally, combined with cotton boll weight, boll opening rate and other information, the cotton yield in the experimental area was estimated by SENP model, and the precision was verified according to the measured data of yield. The experimental results reveal that the U-Net model can effectively extract the information of cotton seedlings from the background with high accuracy. And the precision rate, recall rate and F value reached 93.88%, 97.87% and 95.83% respectively. NDVI based on time series can accurately reflect the growth state of cotton, so as to obtain the predicted boll number of each cotton, which greatly improves the accuracy and universality of the yield estimation model. The determination coefficient (R) of the yield estimation model reached 0.92, indicating that using SENP model for cotton yield estimation is an effective method. This study also proved that the potential and advantage of combining the AWS platform with SENP, due to its powerful cloud computing capacity, especially for deep learning, time-series crop monitoring and large scale yield estimation. This research can provide the reference information for cotton yield estimation and cloud computing platform application.

Cloud-Radio Access Networks (C-RAN) is a novel mobile network architecture where baseband resources are pooled, which is helpful for the operators to deal with the challenges caused by the non-uniform traffic and the fast growing user demands. The main idea of C-RAN is to divide the base stations i…

Cloud-Radio Access Networks (C-RAN) is a novel mobile network architecture where baseband resources are pooled, which is helpful for the operators to deal with the challenges caused by the non-uniform traffic and the fast growing user demands. The main idea of C-RAN is to divide the base stations into the baseband unit (BBU) and the remote radio head (RRH), and then centralize the BBUs to form a BBU pool. The BBU pool is virtualized and shared between the RRHs, improving statistical multiplexing gains by allocating baseband and radio resources dynamically. In this paper, aiming at the problem of resource dynamic allocation and optimization of 5G C-RAN, a resource allocation strategy based on improved adaptive genetic algorithm (IAGA) is proposed. The crossover rate and mutation rate of the genetic algorithm are optimized. Simulation results show that the performance of the proposed resource allocation strategy is better than the common frequency reuse algorithm and the traditional genetic algorithm (GA).

With the development of economy, global demand for electricity is increasing, and the requirements for the stability of the power grid are correspondingly improved. The intelligence of the power grid is an inevitable choice for the research and development of power systems. Aiming at the security …

With the development of economy, global demand for electricity is increasing, and the requirements for the stability of the power grid are correspondingly improved. The intelligence of the power grid is an inevitable choice for the research and development of power systems. Aiming at the security of the smart grid operating environment, this paper proposes a gray-scale image coloring method based on generating anti-network, which is used for intelligent monitoring of network equipment at night, and realizes efficient monitoring of people and environment in different scenarios. Based on the original Generative Adversarial Network, the method uses the Residual Net improved network to improve the integrity of the generated image information, and adds the least squares loss to the generative network to narrow the distance between the sample and the decision boundary. Through the comparison experiments in the self-built CASIA-Plus-Colors high-quality character dataset, it is verified that the proposed method has better performance in colorization of different background images.

With the development of modern agriculture, the clustering phenomenon of greenhouses is prominent. The traditional single greenhouse management is oriented to farmers. It is difficult for upper management to obtain the information of greenhouses conveniently. The real-time transmission of monitorin…

With the development of modern agriculture, the clustering phenomenon of greenhouses is prominent. The traditional single greenhouse management is oriented to farmers. It is difficult for upper management to obtain the information of greenhouses conveniently. The real-time transmission of monitoring results and the real-time regulation of the internal environment of greenhouse clusters are difficult. And the scope of management of large-scale agricultural companies is also growing, and an integrated management platform is urgently needed. The emergence of cloud computing technology has made this management model possible. On the other hand, the greenhouse cluster is a non-linear complex large system, which not only needs to improve the capacity of the greenhouse cluster, but also take into account the utilization of regional resources. The traditional control methods are insufficient in the efficient use of regional resources, and the existing control theory can’t meet the above requirements. Target requirements. The computing power of local equipment can’t meet the needs of massive data processing. Therefore, based on the cloud computing platform, this paper draws on the theory of complex systems to carry out coordinated control theory research on greenhouse clusters, establishes a cloud computing-based greenhouse cluster management system, and designs greenhouse clusters. The control system description model is coordinated; on this basis, the greenhouse cluster coordination control structure model is designed. This study provides a reference for the control of modern greenhouse clusters, and has certain theoretical significance and application value for the development of greenhouse cluster coordinated control theory.

Aimed to reduce the excessive cost of neural network, this paper proposes a lightweight neural network combining dilated convolution and depthwise separable convolution. Firstly, the dilated convolution is used to expand the receptive field during the convolution process while maintaining the numbe…

Aimed to reduce the excessive cost of neural network, this paper proposes a lightweight neural network combining dilated convolution and depthwise separable convolution. Firstly, the dilated convolution is used to expand the receptive field during the convolution process while maintaining the number of convolution parameters, which can extract more high-level global semantic features and improve the classification accuracy of the network. Second, the use of the depthwise separable convolution reduces the network parameters and computational complexity in convolution operations. The experimental results on the CIFAR-10 dataset show that the proposed method improves the classification accuracy of the network while effectively compressing the network size.

Aiming to solve the problem of goods popularity bias, this paper introduces the prevalence of items into user interest modeling, and proposes an item popularity model based on user interest feature. Usually, traditional model that does not take into account the stability of user’s interests, which …

Aiming to solve the problem of goods popularity bias, this paper introduces the prevalence of items into user interest modeling, and proposes an item popularity model based on user interest feature. Usually, traditional model that does not take into account the stability of user’s interests, which leads to the difficulty in capturing their interest. To cope with this limitation, we propose a time-sensitive and stabilized interest similarity model that involves a process of calculating the similarity of user interest. Moreover, by combining those two kinds of similarity model based on weight factors, we develop a novel algorithm for calculation, which is named as IPSTS (IPSTS). To evaluate the proposed approach, experiments are performed and results indicate that Mean Absolute Difference (MAE) and root mean square error (RMSE) could be significantly reduced, when compared with those of traditional collaborative filtering Algorithms.

When searching for the interesting content within a specific website, how to describe the initial need by selecting proper keywords is a critical problem. The character-matching search functions of website can hardly meet users’ requirements. Furthermore, building the content of webpages of a speci…

When searching for the interesting content within a specific website, how to describe the initial need by selecting proper keywords is a critical problem. The character-matching search functions of website can hardly meet users’ requirements. Furthermore, building the content of webpages of a specific web-site and the associated rules is uneconomical. This paper, based on the framework of the Lucene engine, applied a semantic ontology, the calculation of the relevance of word entries, and the semantics of keywords to design an intelligent semantic recommendation system with the Jena secondary semantic analysis technique. Subsequently, the expanded keywords were semantically ranked based on the term frequency analysis technique. Meanwhile, the ontology algorithm and their relevance were introduced as the dynamic weight values. Finally, in the text content retrieval process, the search results were ranked based on the previous relevance weights. The experimental results show that the system designed in this paper is not only easy to develop but also capable of expanding users queries and recommending relevant content. Further, the system can improve the precision and recall for website search results.

Smart grid is viewed as the next-generation electric power system to meet the demand of communication and power delivery in an intelligent manner. With large scale deployment of electric power systems, smart grid faces the challenge from large volume data and high spectrum needs. To realize efficie…

Smart grid is viewed as the next-generation electric power system to meet the demand of communication and power delivery in an intelligent manner. With large scale deployment of electric power systems, smart grid faces the challenge from large volume data and high spectrum needs. To realize efficient spectrum utilization in the fact of spectrum scarcity, cognitive radio (CR) is involved in smart grid and generates the cognitive radio enabled smart grid. Cognitive radio enabled smart grid coexists with primary network by employing CR technologies including spectrum sensing, sharing, access and so on. Spectrum sharing is an important CR technology which realizes network coexistence without harmful interference through radio resource allocation. In this paper, a comprehensive survey is provided to review the state-of-the-art researches on spectrum sharing in cognitive radio enabled smart grid. We identify the network architecture and communication technology issues of cognitive radio enabled smart gird, and illustrate the investigation of spectrum sharing in different radio resource dimensions to highlight the superiority in efficient spectrum utilization.

Energy harvesting (EH) is of prime importance for enabling the Internet of Things (IoT) networks. Although, energy harvesting relays have been considered in the literature, most of the studies do not account for the processing costs, such as the decoding cost in a decode-and-forward (DF) relay. How…

Energy harvesting (EH) is of prime importance for enabling the Internet of Things (IoT) networks. Although, energy harvesting relays have been considered in the literature, most of the studies do not account for the processing costs, such as the decoding cost in a decode-and-forward (DF) relay. However, it is known that the decoding cost amounts to a significant fraction of the circuit power required for receiving a codeword. Hence, in this work, we are motivated to consider an EH-DF relay with the decoding cost and maximize the average number of bits relayed by it with a time-switching architecture. To achieve this, we first propose a frame structure consisting of three phases: (i) an energy harvesting phase, (ii) a reception phase and, (iii) a transmission phase. We obtain optimal length of each of the above phases and communication rates that maximize the average number of bits relayed. We consider the radio frequency (RF) energy to be harvested by the relay is from the dedicated transmitter and the multiple block case when energy is allowed from flow among the blocks, different from the single block case when energy is not allowed to flow among the blocks. By exploiting the convexity of the optimization problem, we derive analytical optimum solutions under the EH scenario. One of the optimal receiving rate for the relay is the same as in single block case. We also provide numerical simulations for verifying our theoretical analysis.